Just like human personal assistants, digital assistants or virtual assistants can perform requested tasks and provide requested advice, information, or services. An assistant's ability to fulfill a user's request is dependent on the assistant's correct comprehension of the request or instructions. Recent advances in natural language processing have enabled users to interact with digital assistants using natural language, in spoken or textual forms, rather than employing a conventional user interface (e.g., menus or programmed commands). Such digital assistants can interpret the user's input to infer the user's intent, translate the inferred intent into actionable tasks and parameters, execute operations or deploy services to perform the tasks, and produce outputs that are intelligible to the user. Ideally, the outputs produced by a digital assistant should fulfill the user's intent expressed during the natural language interaction between the user and the digital assistant.
In order to perform natural language processing on speech inputs, the speech input is first converted to text (e.g., with a speech-to-text processor), and the converted text is then analyzed by a natural language processor to infer the user's intent. Consequently, any errors in the speech-to-text conversion (e.g., incorrect recognition of words in the speech input) will be propagated to the natural language processor, which may be unable to infer the user's intent due to the incorrect transcription of the words. For example, if a user provides a speech input such as “find show times for ‘Hansel and Gretel,”’ and the speech-to-text processor incorrectly converts the input to “find show times for cancel and Gretel,” the natural language processor may be unable to find any movies with the name “cancel and Gretel,” and thus, unable to provide a satisfactory response to the user.
Accordingly, there is a need for systems and methods to infer user intent from a speech input so as to account for possible speech recognition errors.